What is R?

CRAN

https://cran.r-project.org/

https://cran.r-project.org/web/packages/available_packages_by_name.html

https://CRAN.R-project.org/view=Spatial

https://cran.r-project.org/web/views/SpatioTemporal.html

CRAN “Spatial” - three general types of packages

Packages specialised for:

1. accessing geodata and other software functions, interoperability

2. visualisation of geodata

3. analysis of geodata

1) Packages for access and interoperability

• Geospatial Data Abstraction Library (GDAL): rgdal

• GRASS GIS: spgrass6

• SAGA GIS: RSAGA

• Access, PostgreSQL/PostGIS: RODBC, rpostgis, postGIStools

• …

2) Packages for geodata visualisation

• rasterVis

• maps

• plotKML

• plotGoogleMaps

• …

• (also, base, lattice and ggplot general packages)

• … Much more on GitHub

2) Packages for analysis

• point patterns: spatial, spatstat

• geostatistics (2D/3D), likelihood approach, Bayes methods: geoR, gstat, RandomFields

• 2D carthesian geometry operations: Rgeos

• circular, directional statistics: circular

• cluster’s detection: Dcluster

• analytics on greeded data: raster

• geometry on the sphere, great circle analyses: geosphere

• remotely sensed data: remotesensing

• …

R - GIS

• Store, reach geo data
• Visualise
• Manipulate
• Analyse and model geo data

Basic data types in R

R, object oriented language

But we need a model of the real world

“Spatial” in R, short history

• pre-2003, sporadic

• 2003: workshop at DSC

• 2003: start of r-sig-geo

• 2003: rgdal on CRAN

• 2005: sp on CRAN; sp support in rgdal

“Spatial” in R, short history 2

• 2008: Applied Spatial Data Analysis with R

• 2011: rgeos on CRAN

• 2013: second edition of Applied Spatial Data Analysis with R

• 2016-7: simple features for R (finished?)

• 2017-8: spatiotemporal tidy arrays for R (design phase)

Package ‘sp’

First package for classes and methods for spatial data in R

• classes and methods for geo data in R/S language

• does not provide any analytical methods

• S4 data class

• generic methods for points, lines, polygons and grids/surfaces

Clases in ‘sp’ - vector, example for points

Data structure for points

Clases in ‘sp’ - rasters/grids

Data structure for grids

General structure Spatial*DataFrames

• @data - data.frame, attributes

• @coords.nrs - variables in data.frame with coordinate info

• @coords - coordinates

• @bbox - spatial extent

• @proj4string - spatial reference

Methods for ‘sp’ classes

• dimensions(x)

• spTransform(x, CRS)

• bbox(x)

• coordinates(x)

• gridded(x)

• spplot(x)

• over(x, y)

• spsample(x)

• geometry(x)

Simple features - CRAN

Package ‘sf’

Why ‘sp’ was not enough?

But still no support for gridded data!

OGC/ISO - simple features

Simple feature hierarchy

Simple features?

• ISO standard that is widely adopted
• used in spatial databases
• feature: abstraction of real world phenomena
• simple feature: feature with all geometric attributes described piecewise by straight line or planar interpolation between sets of points
• 7 + 10 types, 68 classes, 7 classes ok for 99% of the use cases
• text and binary serialisations (WKT, WKB)
• support for mixed type (GEOMETRYCOLLECTION), and type mix (GEOMETRY)
• support for empty geometry

https://en.wikipedia.org/wiki/DE-9IM

Before ‘sf’ package

Simple feature objects automatically imported as ‘sp’ object using package rgeos

library(rgeos)
my_poly <- readWKT("POLYGON((1 0,0 1,1 2,2 1,1 0))")
plot(my_poly, main="WKT defined polygon")

Start preparing spatial objects from…

Not usual way to prepare geodata this way

#polygons from set of coordinates
Sr1 <- Polygon(cbind(c(2, 4, 4, 1, 2), c(2, 3, 5, 4, 2)))
Sr2 <- Polygon(cbind(c(5, 4, 2, 5), c(2, 3, 2, 2)))
Sr3 <- Polygon(cbind(c(4, 4, 5, 10, 4), c(5, 3, 2, 5, 5)))
Sr4 <- Polygon(cbind(c(5, 6, 6, 5, 5), c(4, 4, 3, 3, 4)), hole = TRUE)
Srs1 <- Polygons(list(Sr1), "s1")
Srs2 <- Polygons(list(Sr2), "s2")
#several polygons into list
Srs3 <- Polygons(list(Sr4, Sr3), "s3/4")
#create spatial polygons, still no CRS/SRID
SpP <- SpatialPolygons(list(Srs1, Srs2, Srs3), 1:3)

Inspect the data…

Stil necessary to attach Spatial information (CRS/SRID) and optionally attributes data.frame

plot(SpP)

Interactive visualisations with Leaflet

… still no CRS/SRID but we can visualise it in different ways

n <-leaflet()
leaflet(height = "300px") %>% addPolygons(data = SpP)

More usually, import the geodata

Import via GDAL/OGR library

HRV_adm2 <- readOGR(getwd(),"HRV_adm2", verbose = TRUE, stringsAsFactors=FALSE, encoding="UTF-8")

OGR data source with driver: ESRI Shapefile 
Source: "C:/Users/hd/PODACI/FER_predavanje", layer: "HRV_adm2"
with 560 features
It has 11 fields

#take a look from how many level 1 administrative division
levels(as.factor(HRV_adm2@data$ID_1))

 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14"
[15] "15" "16" "17" "18" "19" "20" "21"

Manipulate geodata

• Diverse functions from Geos library

• Access functionality of other GIS software

• Result exported via Rgdal (GDAL/OGR library)

Manipulate geodata, union

head(HRV_adm2@data[1:6], n=2)

  ID_0 ISO  NAME_0 ID_1             NAME_1 ID_2
0   57 HRV Croatia    8 Krapinsko-Zagorska  180
1   57 HRV Croatia    8 Krapinsko-Zagorska  181

#levels of variable, ID for county
levels(as.factor(HRV_adm2@data$ID_1))

 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14"
[15] "15" "16" "17" "18" "19" "20" "21"

#make union of polygons, union by county ID
HRV_adm2_union <-  unionSpatialPolygons(HRV_adm2, HRV_adm2@data$ID_1)

Visualise the data

plot(HRV_adm2, col="aliceblue", main="Level 2 administrative polygons",xlim=c(14, 17), ylim=c(42, 47), axes=T, ylab="Latitude", xlab="Longitude")

Visualise the result

plot(HRV_adm2_union, col="aliceblue", main="Level 1 administrative polygons, union",xlim=c(14, 17), ylim=c(42, 47), axes=T, ylab="Latitude", xlab="Longitude")

Integrate data

Attach data from other sources, e.g. Bureau of Statistics

HRV_adm1 <- readOGR("C:\\Users\\hd\\PODACI\\podloge\\GADM\\Croatia\\HRV_adm_shp","HRV_adm1", encoding="Windows-1250",verbose = F, stringsAsFactors=FALSE)
data <- read.xlsx("county_fake_data.xlsx", as.data.frame=T, stringsAsFactors = T,  
                     sheetIndex=1, encoding="UTF-8" )
head(data, n=2)

FALSE    NO ID_COUNTY
FALSE 1 150         1
FALSE 2 345         2

class(data)

FALSE [1] "data.frame"

#merge by spatial polygons
merged_data <- merge(HRV_adm1, data, by.x="ID_1", by.y="ID_COUNTY", all.x=T)
head(merged_data, n=2)

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FALSE    ID_1 ID_0 ISO  NAME_0                 NAME_1 HASC_1 CCN_1 CCA_1
FALSE 1     1   58 HRV Croatia    Ĺ ibensko-Kninska  HR.SB     0  <NA>
FALSE 12    2   58 HRV Croatia Bjelovarska-Bilogorska  HR.BB     0  <NA>
FALSE         TYPE_1 ENGTYPE_1 NL_NAME_1         VARNAME_1  NO
FALSE 1  Županija    County      <NA>        Šibenik 150
FALSE 12 Županija    County      <NA> Bjelovar-Bilagora 345
FALSE 
FALSE Slot "polygons":
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FALSE [634,] 16,50514 45,75876
FALSE [635,] 16,50552 45,75950
FALSE [636,] 16,50563 45,76080
FALSE [637,] 16,50503 45,76207
FALSE [638,] 16,50318 45,76354
FALSE [639,] 16,49641 45,76646
FALSE [640,] 16,49171 45,76934
FALSE [641,] 16,48685 45,77170
FALSE [642,] 16,47865 45,77710
FALSE [643,] 16,49106 45,77910
FALSE [644,] 16,49287 45,77986
FALSE [645,] 16,49401 45,78093
FALSE [646,] 16,49493 45,78276
FALSE [647,] 16,49521 45,78466
FALSE [648,] 16,49422 45,78996
FALSE [649,] 16,49401 45,79414
FALSE [650,] 16,49474 45,79638
FALSE [651,] 16,49622 45,79766
FALSE [652,] 16,49755 45,79809
FALSE [653,] 16,49917 45,79811
FALSE [654,] 16,50101 45,79763
FALSE [655,] 16,50422 45,79638
FALSE [656,] 16,50654 45,79581
FALSE [657,] 16,50921 45,79573
FALSE [658,] 16,51107 45,79605
FALSE [659,] 16,51335 45,79673
FALSE [660,] 16,51919 45,79917
FALSE [661,] 16,52448 45,80047
FALSE [662,] 16,52792 45,80157
FALSE [663,] 16,53020 45,80269
FALSE [664,] 16,53307 45,80495
FALSE [665,] 16,53444 45,80577
FALSE [666,] 16,53638 45,80650
FALSE [667,] 16,53814 45,80681
FALSE [668,] 16,54081 45,80671
FALSE [669,] 16,54422 45,80582
FALSE [670,] 16,54563 45,80501
FALSE [671,] 16,54722 45,80358
FALSE [672,] 16,54856 45,80277
FALSE [673,] 16,55513 45,80042
FALSE [674,] 16,55984 45,79723
FALSE [675,] 16,56236 45,79467
FALSE [676,] 16,56353 45,79376
FALSE [677,] 16,57182 45,78978
FALSE [678,] 16,57511 45,78911
FALSE [679,] 16,57872 45,78910
FALSE [680,] 16,58089 45,78941
FALSE [681,] 16,58568 45,79046
FALSE [682,] 16,58865 45,79129
FALSE [683,] 16,59038 45,79227
FALSE [684,] 16,59104 45,79287
FALSE [685,] 16,59153 45,79353
FALSE [686,] 16,59183 45,79471
FALSE [687,] 16,59112 45,79621
FALSE [688,] 16,58846 45,79803
FALSE [689,] 16,58747 45,79902
FALSE [690,] 16,58675 45,80119
FALSE [691,] 16,58692 45,80359
FALSE [692,] 16,58817 45,80779
FALSE [693,] 16,58823 45,81030
FALSE [694,] 16,58768 45,81434
FALSE [695,] 16,58764 45,81710
FALSE [696,] 16,58800 45,81981
FALSE [697,] 16,58878 45,82202
FALSE [698,] 16,59219 45,82223
FALSE [699,] 16,59536 45,82271
FALSE [700,] 16,59875 45,82375
FALSE [701,] 16,60074 45,82492
FALSE [702,] 16,60209 45,82624
FALSE [703,] 16,60744 45,83355
FALSE [704,] 16,61135 45,83840
FALSE [705,] 16,61728 45,84350
FALSE [706,] 16,61822 45,84397
FALSE [707,] 16,62018 45,84444
FALSE [708,] 16,62301 45,84442
FALSE [709,] 16,62500 45,84401
FALSE [710,] 16,62679 45,84311
FALSE [711,] 16,62745 45,84243
FALSE [712,] 16,62824 45,84015
FALSE [713,] 16,62796 45,83504
FALSE [714,] 16,62826 45,83217
FALSE [715,] 16,62925 45,83009
FALSE [716,] 16,63101 45,82892
FALSE [717,] 16,63321 45,82851
FALSE [718,] 16,63478 45,82874
FALSE [719,] 16,63617 45,82932
FALSE [720,] 16,63920 45,83151
FALSE [721,] 16,64118 45,83247
FALSE [722,] 16,64345 45,83302
FALSE [723,] 16,64588 45,83322
FALSE [724,] 16,64950 45,83294
FALSE [725,] 16,65166 45,83226
FALSE [726,] 16,65297 45,83149
FALSE [727,] 16,65597 45,82907
FALSE [728,] 16,66204 45,83333
FALSE [729,] 16,66252 45,83515
FALSE [730,] 16,66340 45,83690
FALSE [731,] 16,66490 45,83834
FALSE [732,] 16,66700 45,83945
FALSE [733,] 16,67119 45,84077
FALSE [734,] 16,67760 45,84231
FALSE [735,] 16,68198 45,84276
FALSE [736,] 16,68804 45,84288
FALSE [737,] 16,69019 45,84591
FALSE [738,] 16,69701 45,85410
FALSE [739,] 16,69762 45,85556
FALSE [740,] 16,69771 45,85703
FALSE [741,] 16,69674 45,85969
FALSE [742,] 16,69332 45,86525
FALSE [743,] 16,68868 45,86938
FALSE [744,] 16,68446 45,87263
FALSE [745,] 16,68347 45,87407
FALSE [746,] 16,68285 45,87568
FALSE [747,] 16,68071 45,88296
FALSE [748,] 16,68053 45,88509
FALSE [749,] 16,68082 45,88652
FALSE [750,] 16,68241 45,88994
FALSE [751,] 16,68303 45,89185
FALSE [752,] 16,68305 45,89710
FALSE [753,] 16,68354 45,90056
FALSE [754,] 16,68251 45,90641
FALSE [755,] 16,68229 45,91207
FALSE [756,] 16,68271 45,91666
FALSE [757,] 16,68374 45,91990
FALSE [758,] 16,68484 45,92312
FALSE [759,] 16,68515 45,92667
FALSE [760,] 16,68468 45,92921
FALSE [761,] 16,68326 45,93274
FALSE [762,] 16,68302 45,93595
FALSE [763,] 16,68326 45,93750
FALSE [764,] 16,68381 45,93891
FALSE [765,] 16,68603 45,94135
FALSE [766,] 16,68701 45,94328
FALSE [767,] 16,68714 45,94548
FALSE [768,] 16,68663 45,94884
FALSE [769,] 16,68763 45,95380
FALSE [770,] 16,68728 45,95613
FALSE [771,] 16,68575 45,95951
FALSE [772,] 16,68419 45,96485
FALSE [773,] 16,68400 45,96733
FALSE [774,] 16,68424 45,96983
FALSE [775,] 16,68505 45,97223
FALSE [776,] 16,68970 45,97919
FALSE [777,] 16,69035 45,98075
FALSE [778,] 16,69163 45,98558
FALSE [779,] 16,69236 45,98711
FALSE [780,] 16,69601 45,99130
FALSE [781,] 16,69693 45,99280
FALSE [782,] 16,69747 45,99437
FALSE [783,] 16,69788 45,99682
FALSE [784,] 16,69812 46,00353
FALSE [785,] 16,70790 46,00889
FALSE [786,] 16,71024 46,01376
FALSE [787,] 16,71089 46,01610
FALSE [788,] 16,71099 46,01859
FALSE [789,] 16,71040 46,02318
FALSE [790,] 16,71111 46,02700
FALSE [791,] 16,71111 46,02853
FALSE [792,] 16,71075 46,03007
FALSE [793,] 16,71010 46,03138
FALSE [794,] 16,70526 46,03873
FALSE [795,] 16,71677 46,04159
FALSE [796,] 16,73101 46,04612
FALSE [797,] 16,73299 46,04712
FALSE [798,] 16,73466 46,04830
FALSE [799,] 16,73808 46,05201
FALSE [800,] 16,73947 46,05290
FALSE [801,] 16,74138 46,05364
FALSE [802,] 16,74836 46,05551
FALSE [803,] 16,75711 46,05696
FALSE [804,] 16,76719 46,05996
FALSE [805,] 16,76936 46,06038
FALSE [806,] 16,77218 46,06033
FALSE [807,] 16,77879 46,05869
FALSE [808,] 16,78061 46,05800
FALSE [809,] 16,78200 46,05683
FALSE [810,] 16,78257 46,05538
FALSE [811,] 16,78234 46,05306
FALSE [812,] 16,77963 46,04794
FALSE [813,] 16,77970 46,04773
FALSE [814,] 16,78782 46,04792
FALSE [815,] 16,79047 46,04824
FALSE [816,] 16,79364 46,04908
FALSE [817,] 16,79758 46,05085
FALSE [818,] 16,80283 46,05500
FALSE [819,] 16,80510 46,05640
FALSE [820,] 16,82063 46,06228
FALSE [821,] 16,82343 46,06360
FALSE [822,] 16,82557 46,06510
FALSE [823,] 16,82643 46,06609
FALSE [824,] 16,82714 46,06746
FALSE [825,] 16,82737 46,06974
FALSE [826,] 16,82583 46,07478
FALSE [827,] 16,82495 46,08412
FALSE [828,] 16,82314 46,09008
FALSE [829,] 16,83172 46,09075
FALSE [830,] 16,83846 46,09102
FALSE [831,] 16,83923 46,08923
FALSE [832,] 16,84026 46,08815
FALSE [833,] 16,84112 46,08768
FALSE [834,] 16,84324 46,08707
FALSE [835,] 16,85552 46,08607
FALSE [836,] 16,86201 46,08594
FALSE [837,] 16,86408 46,08435
FALSE [838,] 16,86749 46,08240
FALSE [839,] 16,86856 46,08124
FALSE [840,] 16,86887 46,08052
FALSE [841,] 16,86905 46,07895
FALSE [842,] 16,86875 46,07735
FALSE [843,] 16,86597 46,07205
FALSE [844,] 16,86587 46,07009
FALSE [845,] 16,86635 46,06801
FALSE [846,] 16,86716 46,06686
FALSE [847,] 16,86850 46,06591
FALSE [848,] 16,87413 46,06470
FALSE [849,] 16,87557 46,06394
FALSE [850,] 16,87716 46,06241
FALSE [851,] 16,87953 46,05891
FALSE [852,] 16,88000 46,05714
FALSE [853,] 16,88030 46,05231
FALSE [854,] 16,88161 46,04982
FALSE [855,] 16,88324 46,04853
FALSE 
FALSE 
FALSE 
FALSE Slot "plotOrder":
FALSE [1] 1
FALSE 
FALSE Slot "labpt":
FALSE [1] 16,97666 45,73985
FALSE 
FALSE Slot "ID":
FALSE [1] "1"
FALSE 
FALSE Slot "area":
FALSE [1] 0,3074844
FALSE 
FALSE 
FALSE 
FALSE Slot "plotOrder":
FALSE [1] 1 2
FALSE 
FALSE Slot "bbox":
FALSE        min      max
FALSE x 15,52597 17,47081
FALSE y 43,48680 46,09102
FALSE 
FALSE Slot "proj4string":
FALSE CRS arguments:
FALSE  +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

class(merged_data)

FALSE [1] "SpatialPolygonsDataFrame"
FALSE attr(,"package")
FALSE [1] "sp"

Visualise the result

plot(merged_data[13], col=colorRampPalette(brewer.pal(5,"Blues"))(21), axes=T, main="Variable NO")

Example, select Lobor and Oroslavje

head(HRV_adm2@data[1:4], n=2)

  ID_0 ISO  NAME_0 ID_1
0   57 HRV Croatia    8
1   57 HRV Croatia    8

select <- HRV_adm2[HRV_adm2@data$NAME_2=="Lobor" | HRV_adm2@data$NAME_2=="Oroslavje",]
class(select)

[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"

#prepare random sample in selection
p_100 <- spsample(select,100, "random") 

Visualise the result

plot(select, main="Lobor and Oroslavje")
points(p_100, pch=15, cex=0.9, col="green4")

Prepare additional data, spTransform

When data not on the same CRS

ETRS_LEA <- CRS("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs")
#sp transform to European equal area projection as dem
HRV_adm1_ETRS <- spTransform(HRV_adm1, ETRS_LEA)
#spatial extents
bbox(HRV_adm1_ETRS)

  min     max

x 4594092 5061199 y 2163680 2624962

bbox(HRV_adm1)

   min      max

x 13,48958 19,43518 y 42,38543 46,55052

Export some elements

Export selected polygons as Esri shapefile, KML, PostgreSQL/PostGIS…

writeOGR (select, ".", "Lobor_Orosavje", driver="ESRI Shapefile", overwrite=T)
writeOGR (select, "Lobor_Orosavje.kml", ".", driver="KML", overwrite=T)
writeOGR(select, "PG:dbname=GEO_meetup", layer_options = "geometry_name=geom", "states", "PostgreSQL")

Example: additional customisation for Leaflet

HRV_adm0 <- readOGR("C:\\Users\\hd\\PODACI\\FER_predavanje\\HRV_adm_shp","HRV_adm0", verbose = F, stringsAsFactors=FALSE, encoding="UTF-8")
HRV_adm1 <- readOGR("C:\\Users\\hd\\PODACI\\FER_predavanje\\HRV_adm_shp","HRV_adm1", verbose = F, stringsAsFactors=FALSE, encoding="UTF-8")
HRV_adm2 <- readOGR("C:\\Users\\hd\\PODACI\\FER_predavanje\\HRV_adm_shp","HRV_adm2", verbose = F, stringsAsFactors=FALSE, encoding="UTF-8")
#create icon
rest_icon <- makeIcon(iconUrl = "R_logo.png",
  iconWidth = 12, iconHeight = 12,
  iconAnchorX = 15, iconAnchorY = 10)
#make our own pallette
colors<- colorRampPalette(brewer.pal(5,"Blues"))(21)
#points to visualise
load("df.RData")
#make it spatial
coordinates(df) <- ~ lng+lat
#what is CRS?
proj4string(df) <- CRS("+proj=longlat +datum=WGS84") #EPSG:4326

Our data in Leaflet

m <- leaflet()  %>% addTiles()
m<- m %>% addPolygons(data=HRV_adm1, fillColor = colors, stroke = F, fillOpacity = 0.5,weight=3)
m<- m %>% addPolygons(data=HRV_adm0, fill=F, color="#f93", stroke = T, fillOpacity = 0.5,weight=1)
m<- m %>% addPolygons(data=HRV_adm2, fill=F, color="gray", stroke = T, fillOpacity = 0.5,weight=1)
m<- m %>% addMarkers(coordinates(df)[,1],coordinates(df)[,2], icon = rest_icon)
m
#library(htmlwidgets)
#saveWidget(m, file="m.html")

Link m html

Use capture from Leaflet for dynamic documents from RMarkdown